Analysis of skewness and kurtosis for ambient air quality monitoring data Neyveli thermal plants

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1 Journal of Scientific & Industrial Research Vol. 63, September 2004, pp Analysis of skewness and kurtosis for ambient air quality monitoring data Neyveli thermal plants G Sriram*, N Krishnamohan** and V Gopalasamy*** Department of Mechanical Engineering, SCSVMV (Deemed University) Enathur, Kanchipuram Received 10 October 2003; rev recd 14 June 2004; accepted 07 July 2004 Air pollution adversely affect biological, physical, and human respiratory systems. Monitoring variations of ambient air quality is therefore essential. Like application of probability analysis in the design of mechanical dust collectors this type of analysis can be applied in other ways in air pollution work. Positively skewed distributions are characteristics of naturally occurring phenomena such as, rain fall intensity, wind force, and stream flow. In air pollution phenomena, a good many data are of this type. Dust fall and atmospheric particulate and gaseous concentrations are typical examples. Knowledge of pollution distribution and direction is important when choosing air sampling station. Keeping this in view the air quality study made in September 1988 is analyzed in respect of sulphur dioxide and suspended particulate matter in the ambient air in the neighbourhood of Neyveli Thermal Power Corporation for finding ideal location of sampling stations. Keywords: Analysis, Air quality, Thermal plants Introduction Man s activities have affected the natural air resources so severely in several areas of the globe that strict measures of control and statement are essential to prevent further threats to human health, animals, plants, ecosystems, and materials. Meteorological and topographical conditions may add to and increase the undesirable resultant air quality effects. On the other hand, pollutants dispersed into the atmosphere exert both local and global scale influences on climate. There is lot of evidence that air pollution has adverse effects on man s health and well-being, on animals, plants and materials, on other environmental media, and climate. Despite efforts to reduce emissions from particular sources and of certain pollutants, both the amount of air pollution and the number of different contaminating substances have increased due to the growth of the population and industrialization. Additional and co-ordinated activities to control air pollution are a subject of immediate concern to the industrialized countries, but they should be of equal concern to other counties in the world. A short term air quality survey was conducted in 1988 for 52 d in and around the Neyveli Lignite *Author for correspondence **Department of Mechanical Engineering, Annamalai University, Annamalai ***Department of Civil Engineering, Annamalai University, Annamalai Corporation (NLC) campus, at Neyveli from 18 July 1988 to 08 September 1988, in order to measure the prevailing air quality in the area. Ambient air concentrations of the three major pollutants, relevant to a thermal power plant situation, namely, sulphur dioxide (SO 2 ), Nitrogen dioxide (NO 2 ), and suspended particulate mater (SPM) were measured on a continuous basis, in order to gather the continuous base line air quality data. Twenty seven sampling stations were located in various directions with respect to the existing sources, inclusive of thermal station (I), thermal station (II) and other industrial sources existing in the NLC Campus. Methodology The standard high volume air sampler was used for monitoring SPM, SO 2, and NO x measurement with gaseous sampling arrangement. The samples of SO 2 and NO x were observed, using sodium tetrachloromercurate and sodium hydroxide solution, respectively. The SPM were analyzed by gravemetric method and SO 2 and NO x calorimetrically, by using spectrophotometer, according to the standard method. Air Sampling Stations Air sampling instruments were placed at various distances from the thermal station II in NLC Campus, in such a way that all the station sectors were represented. Most of the 27 sampling stations were located on the 22.5 direction-sector.

2 770 J SCI IND RES VOL 63 SEPTEMBER 2004 The relative locations of the various sampling stations can be seen in Fig. 1. Sampling instruments were placed on the roof top of the buildings in such a way that the sampling height was about 3.0 m above ground level. Meteorological Situations The most predominant wind direction during the period of study was South-South-West (SSW), where wind velocity, varied from 0.2m/s (0.7 km/h) to 2.4 m/s (8.6 km/h), and calm or no-wind conditions had prevailed for about 4.7 per cent of the time periods. Cloud cover varied between 1/8 to 8/8. The dry bulb air temperature varied from 22.2 to 27.2 C. The relative humidity varied from 37 to 96 per cent during the period of study. Direction Percentage Frequency Table 1 Wind data Kurto sis Skew ness Shape factor (k) Scale factor (c) NNE NE ENE 6.8 E ESE 3.0 SE SSE 3.9 S SSW SW WSW W WNW NW NNW N Interpretation of Data The most predominant wind directions are WNW and SSW. The effect of wind direction is to determine the direction of released pollutants. Because wind direction is the direction from which the wind blows, a west wind would cause pollution to move toward the east from the source (Table 1). Hence, it is considered in this study the concentrations of SO 2 and SPM in the stations located within 0 to 180 o (N to S). The stations located at various distances from the emission sources are picked up distance-wise and measures of skewness and kurtosis were found from the observed data (Table 2 and 3) to bring out characteristic feature of the data. The measure of skewness shows the direction and the extent of asymmetry in a series, and allows to compare two or more series with regard to these. Kurtosis refers to the peakedness of a frequency curve. Measure of kurtosis uses the peakedness of the normal curve as a reference. The predominant wind directions are WNW and SSW. Skewness decreases in the direction in which the wind blows most of the time ( ). Also kurtosis value is lowest (-1.074). The Weibull parameter k (Scale factor) in the WNW direction is high, indicating that there is less wind speed variation. The kurtosis and skewness show the direction of wind that prevailed during the sampling period. SPM Concentration One of the products of modern development is the atmospheric pollution. By definition, suspended particulate are too small in size to have appreciable falling velocity and are likely to remain in the Table 2 Kurtosis : Suspended particles and sulphur dioxide Station no Distance km Kurtosis SPM SO Table 3 Skewness : Suspended particles and sulphur dioxide Station no Distance km Skewness SPM SO

3 SRIRAM et al.: AMBIENT AIR QUALITY MONITORING DATA 771 Fig. 1 Lay out of air sampling stations

4 772 J SCI IND RES VOL 63 SEPTEMBER 2004 atmosphere for significant periods of time. These particulate usually ranges from 1 to approx. 100 µ to size and may be caused by various processes such as, incomplete combustion of solid, liquid or gaseous fuels, waste from metallurgical, chemical and refining operations, incineration, etc. Moreover, natural sources also contribute to suspended materials like, spores, salt water spray, and pollens. High volume sampling is an internationally accepted standard technique monitoring the concentration suspended particulate. In these systems a large volume (1500 cu.m) of atmospheric air is passed through a suitable filter medium for 24h they thus yield measurable dust samples in areas with dust levels as low as 1µg for cubic meter of air. The mass concentration of suspended particulate in ambient air, expressed in µg in cubic meter is calculated by measuring the mass of collected particulate and the volume of air sample. In general, numerical summary measures will be the first quantities to be computed from a new and unfamiliar set of data. Also numerical summary measures are quick and easy to compute and display. The numerical summaries can be sub divided into measures of location spread and symmetry. Symmetry describes that to which the data values are evenly distributed about their center. For essentially symmetric data the skewness coefficient becomes near zero. In the present study the predominant wind direction (WNW, SSW), skewness, and kurtosis value are almost close to zero, for SPM. Fig. 2a and 2b show p-p plots for Gaussian distribution fit to the SPM data. SO 2 Concentration In the predominant wind directions (WNW, SSW) skewness and kurtosis values of SO 2 are around three and kurtosis values are around two and distribution is skewed to the right. A skewness value of zero indicates the distribution is symmetrical. Thus, it is observed that there is a minor difference between suspended particulate matter and gases in respect of dispersion in the direction of wind. Table 4 Standard deviation: Suspended particles and sulphur dioxide Station no Distance km Standard deviation SPM SO Table 5 Numerical value of kurtosis and skewness coefficient Direction Percentage frequency Kurtosis Skewness WNW W WSW SE SSW NW Also there is almost uniform value noted in the SO 2 concentration of standard deviation with distance (Table 4), whereas it is higher in the case of SPM. This indicates that particulate is settled within shorter distance compared to SO 2. Conclusions (i) The kurtosis and skewness coefficient corresponding to the different directions of wind shows important fluctuations. It can be observed that most of the skewness coefficients are positive, therefore wind speed values are grouped around values below the mean. Both kurtosis and skewness show a decreasing trend as one direction becomes more dominant (Table 5). (ii) The effect of wind direction is to determine the direction of released pollutants On the basis of the above facts this analysis on concentration of pollutants (SO 2 and SPM) recorded at ten stations located in the wind direction revealed that: (a) Skewness values of zero indicate the symmetrical distribution. Kurtosis value of SO 2 is around three and skewness value is around two and it is known as positive skewed distribution. Here the mean is greater than the median indicating that positive skewed distributions are characteristic of naturally occurring phenomena. (b) In the predominant wind direction skewness values are slightly skewed positively in the case of SPM.

5 SRIRAM et al.: AMBIENT AIR QUALITY MONITORING DATA 773 Fig. 2a P P plots for gaussian distribution fit to the SPM data points for the gaussian distribution closer to the 11 line indicating a better fit to the data

6 774 J SCI IND RES VOL 63 SEPTEMBER 2004 Fig. 2b P plots for gaussian distribution fit to the SPM points for the gaussian distribution closer to the 1:1 line indicating a better fit to the data

7 SRIRAM et al.: AMBIENT AIR QUALITY MONITORING DATA 775 (iii) For periodical impact analysis study on air pollution by thermal power plant in addition to predicting concentration on the basis of wind direction and velocity, it is possible to find the wind direction by finding kurtosis and skewness of each pollutant. (iv) The record of air sampling station which give lesser kurtosis and skewness of pollutant is the direction of prevailing wind. (v) Thus, it is possible to optimize the sampling station for regular pollution watch scientifically and thereby savings in time and money. (vi) Some more precise information about pollutant dispersion could be obtained and it is possible to optimize the number of air sampling stations if the wind directions are measured in all the air sampling stations instead of depending only on meteorological stations near to the sampling stations. References 1 Magill Paul L, Holden Francis R & Ackley Charles, Air pollution hand book (McGraw-Hill Book Company, Inc., London) Chockalingam M P, Environmental impact assessment, in respect of air quality, in the neighborhood of the second thermal station at Neyveli, Draft Report of Annamalai University, Turner D Burce, Workbook of atmospheric dispersion estimate(crc Press Inc.) Torres J L, Garcia A, Prieto E & Francisco A De, Characterization of wind speed data according to wind direction, J Solar Ener Eng, (1999) IS:5182 (Part II), Indian Standard Methods for measurement of AG quality, IS:5182 (part VI), Indian Standard methods for measurement of air quality, Daniel S, Wilks Statistical methods in the atmospheric sciences An introduction (Academic press. New York) 1995.